Protocols:Motif Activity Response Analysis(MARA)
From FANTOM5_SSTAR
Calculating motif activities in 7 easy steps
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Last updated: 2012.03.29
OSC Table input file requirements
Your OSC Table file should follow the general OSC Table file requirements. In particular, note that the first column of the OSC Table file should be the cluster identifier. The name of this column should be "id".
In addition,
1. The header section of the OSC Table file should specify the genome assembly
that was used;
2. The data section should contain the normalized (tags-per-million) expression
data (columns containing raw data will be ignored);
3. The data section should contain a column labeled 'pos' that shows the
representative position within each promoter. Usually this representative position is defined as the most highly expressed position within a promoter, but you can choose the criterion as you want. The position should be a zero-based coordinate.
If you have microarray data instead of CAGE data, you can convert them
into the appropriate OSC Table file format using the script
convert_microarray.py. Note that this script is still in development and may not
work flawlessly. In theory it should work as follows:
python convert_microarray.py <assembly> <input_filename>
where <assembly> is the genome assembly name, and <input_filename> is the name of the file containing the microarray data. This script will write out a single file level2.osc in the OSC Table file format.
Step 1: Calculate the binding profile of TFs with respect to the TSS
Use the script make_profile.py to calculate for each TF the binding profile with respect to the transcription start site. If you are lazy, instead of calculating this yourself you can also use the binding profile as calculated using the FANTOM5 data. To calculate the binding profiles yourself, use
python make_profile.py [-t <tfbs>] [-o <output>] [-p] level2.osc[.gz|.bz2]
where level2.osc[.gz|.bz2] is the OSC Table file containing your CAGE or microarray data. This OSC Table file should follow the generic OSC Table file format definition (see http://fantom.gsc.riken.jp/4/download/Tables/doc/). It should also declare the genome assembly in the header section, as in this example:
- ParameterValue[genome_assembly] = hg19
Also, in the data section, the representative position for each promoter should be shown in a column labeled "pos" (without the quotes). In most cases, the representative position of a promoter is defined as its most highly expressed position, though other definitions can in principle be used. In the current version of the motif activity pipeline, there are no restrictions on the name of the promoter (as shown in the first column in the data section of the OSC Table file).
The script makes use of the precalculated TFBSs stored in the file <assembly>.sites.bed located in /osc-fs_home/scratch/mdehoon/Data/TFBS; you can use the -t option to specify a different directory in which to look for this file. This script creates a single file containing the binding profiles for all transcription factors in a tab-delimited format. Use the -p option if you want to create figures for each of the binding profiles.
Step 2: Associate TFBSs with promoters
Use the script associate_tfbs.py to associate predicted TFBSs to CAGE promoters:
python associate_tfbs.py [-t <tfbs>] [-o <output>] [-p <profile_filename>]
level2.osc[.gz|.bz2]
where level2.osc[.gz|.bz2] is the OSC file containing your CAGE or microarray data, and <profile_filename> is the name of the file you created in Step 1. This script also makes use of the precalculated TFBSs stored in the file <assembly>.sites.bed located in /osc-fs_home/scratch/mdehoon/Data/TFBS; you can use the -t option to specify a different directory in which to look for this file. This script creates a single file containing the associations of predicted TFBSs to promoters.
Step 3: Calculate the motif activities
We are now ready to calculate the motif activities:
python mara.py -t <tfbs> [-o <output>] [-n <ntfbs>] level2.osc[.gz|.bz2]
where level2.osc[.gz|.bz2] is the OSC file containing your CAGE or microarray data, and <tfbs> is the name of the file you created in Step 2. With the -n option you can specify the threshold on the number of predicted TFBSs for each motif, which defaults to 150. This means that motifs with less than 150 predicted binding sites are discarded. This script will generate a single file containing the motif activities in each condition, their standard deviations, and their overall Z-scores.
Step 4: Calculate the network as predicted from the motif activities
Calculate the MARA network using the script make_network.py:
python make_network.py -a <activity> -t <tfbs> [-z <threshold>]
[-o <output>] level2.osc[.gz|.bz2]
where level2.osc[.gz|.bz2] is the OSC file containing your CAGE or microarray data, <activity> is the name of the file containing the motif activities (which you calculated in Step 3), and <tfbs> is the name of the file containing the predicted TFBSs for each promoter (which you calculated in Step 2). You can specify a threshold on the Z-score on the network edges; this threshold defaults to 1.5. This script creates a single file containing the motif-to-promoter network as calculated by MARA.
Step 5: Convert the MARA network to a Cytoscape-loadable file
To view the network in Cytoscape, the MARA network file needs to be converted to an input file in the proper format for Cytoscape. For this purpose you can use the convert_cytoscape.py script:
python convert_cytoscape.py [-o <output>] level2.osc[.gz|.bz2]
level2.network
where level2.osc[.gz|.bz2] is the OSC file containing your CAGE or microarray data, and level2.network is the name of the file you created in Step 4. In this step, the OSC file is only used to determine the genome assembly. The resulting Cytoscape input file corresponds to a motif-to-promoter network as extracted from the full MARA network.
Step 6: Find the top motifs in each experimental condition
For this purpose, we calculate the Z-score for each motif in each experimental condition, and we sort the motifs based on their Z-score:
python sort_motifs.py [-o <output>] <input_filename>
where <input_filename> is the name of the file containing the motif activities, which you calculated in Step 3.
Step 7: Create subnetworks for each experimental condition
From the full MARA network we now extract subnetworks for each experimental condition, showing only the top motifs in each:
python write_top_networks.py [-o <output_directory>]
<cytoscape_filename> <sortedtfs_filename>
where <cytoscape_filename> is the name of the Cytoscape input file generated in Step 4, and <sortedtfs_filename> is the file containing the top motifs in each experimental condition generated in Step 6. This script will write out one Cytoscape input file for each experimental condition in <sortedtfs_filename>.
